Technical Field
The present invention relates to an uplink pilot sequence allocation, particularly to a user-eigen-space-based intercell cooperative uplink pilot sequence allocation method in a massive MIMO system, and also to a base station using the method, which belong to the field of wireless communication technologies.
Related Art
As shown in FIG. 1, in the existing multi-cell massive MU-MIMO system, suppose that there are L cells each having one N-antenna base station (BS) and K single-antenna users. Suppose that all L cells use identical K pilot sequences, which are represented with a τ×K-dimensional orthogonal matrix Φ, satisfying ΦHΦ=τI. Moreover, suppose that pilot transmission of different cells is synchronous, a channel matrix is:
            H      ^              i      ,      i        =                    1                                            ρ              p                                ⁢          τ                    ⁢              Y        i        p            ⁢              Φ        *              =                  H                  i          ,          i                    +                        ∑                      l            ≠            i                          ⁢                                  ⁢                  H                      i            ,            l                              +                        1                                                    υ                p                            ⁢              τ                                      ⁢                  N          i          p                ⁢                  Φ          *                    
ΦHΦ=τI is used in the derivation of the above formula. Kth-column ĥi,k,i of Ĥi,i is an estimate value of a channel vector hi,k,i. It is thus clear that ĥi,k,i is a linear combination of user channel vectors hi,k,l (l=1, . . . , L) using identical pilot sequences in different cells, and such a phenomenon is referred to as pilot contamination. As the pilot contamination leads to a channel estimation error, not only is detection performance of uplink signals reduced, but also undesired users are interfered a lot if downlink precoding is performed by using the channel matrix.
FIG. 2 gives a schematic diagram of uplink pilot contamination in a massive MIMO system. With respect to a base station 1, if User 1 in Cell 1 and User 2 in Cell 2 use an identical uplink pilot sequence, as the base station 1 cannot separate signals (having the same eigen-space) of the two users, a pilot contamination problem is produced. On the other hand, if User 1 in Cell 1 and User 4 in Cell 2 use an identical uplink pilot sequence, the base station is easy to separate signals (having different eigen-spaces) of the two users by eigen-space separation. At this point, a pilot contamination problem may not be produced. It is thus clear that pilot contamination is produced between adjacent edge users of a neighboring cell, and thus it is necessary to allocate different pilot sequences to the users. On the other hand, for users in the center of the cell, it is easy to solve the pilot contamination problem thereof (e.g., by eigen-space separation), and thus identical pilot sequences can be allocated to the users.
As pilot contamination is produced between adjacent edge users of a neighboring cell as the users have identical eigen-spaces such that the base station cannot separate uplink signals of different users. Therefore, it is only necessary to consider how to eliminate pilot contamination between cell edge users.
With respect to the uplink pilot contamination in the massive MIMO system, some solutions of suppressing pilot contamination have been put forward. Several typical solutions are introduced below, and their deficiencies are described at the same time.
(1) Protocol Based Pilot Contamination Suppression Method
The most direct method is to reduce a pilot contamination effect by frequency multiplexing or reducing users using non-orthogonal pilot sequences. However, generally, the frequency multiplexing cannot make use of the advantage that the massive MIMO system can multiplex a great number of users at the same time, this is because the number of users served decreases although the method improves SINR of particular users.
The existing time-shift (nonsynchronous)-transmission-protocol based solution has a basic idea of dividing a cell into several groups A1, . . . , AΓ, and using a time-shift transmission protocol in the groups, which is described in FIG. 3 by giving an example of Γ=3. When users in the group A1 transmits a pilot, a base station (BS) in the group A2 sends downlink data signals. This avoids pilot contamination between users in the group A1 and the group A2. Meanwhile, a base station in the group A1 needs to estimate a channel matrix of the users in the group A1 when downlink sending signals from base stations in which the group A2 and the group A3 are present. Since the downlink transmitting power ρd is generally greater than the power ρP of a pilot sequence, it is not clear at present how much gain the method can provide.
(2) Angle of Arrival (ADA) Based Pilot Contamination Suppression Method
Studies show that, in an actual channel model, interference may not exist between some users having identical or non-orthogonal pilot sequences. According to a multi-path channel model about a uniform linear array (ULA), an array steering vector may be expressed as:
      a    ⁡          (      θ      )        =      [                            1                                                  e                                          -                j                            ⁢                                                          ⁢              2              ⁢              π              ⁢                              D                λ                            ⁢                              cos                ⁡                                  (                  θ                  )                                                                                          ⋮                                                  e                                          -                j                            ⁢                                                          ⁢              2              ⁢              π              ⁢                                                                    (                                          N                      -                      1                                        )                                    ⁢                  D                                λ                            ⁢                              cos                ⁡                                  (                  θ                  )                                                                          ]  
In the above formula, D is an antenna distance, λ is a carrier wavelength, and θ is a random AOA of f(θ) having a probability density function (PDF). It is well documented that, even if users use identical pilot sequences, it is difficult for their pilots to interfere with each other as long as they have non-overlapping AOA PDFs. Therefore, a solution of reducing pilot contamination is proposed in some documents that identical pilot sequences are allocated to the users having non-overlapping AOA PDFs. However, the method requires a related matrixes between respective users of respective base stations to satisfy a condition that their primary eigen-spaces do not overlap and requires the base stations to know covariance matrixes between them and all the users. That is to say, if there are L base stations and K users in each base station, it is required that the base stations could obtain LK covariance matrixes between them and the LK users. However, in fact, it is difficult for the base stations to acquire information of the LK covariance matrixes. In addition, when two users in different cells to a certain base station have similar AOAs, the base station cannot distinguish the two users.
(3) Eigen-Space Division Based Blind Pilot Contamination Suppression Method
An eigenvalue decomposition (EVD) channel estimation and iterative projection least squares based channel vector estimation method is proposed in some documents. Such a eigen-space division technology based and EVD based channel estimation method requires that channel vectors of different users should be orthogonal, and this assumption enables the base station to estimate channel vectors by receiving the amount of statistics of data.
However, if two users using identical pilot sequences in adjacent two cells are both located at edges and very close to each other, the two users may be in the same eigen-space, and it is difficult to separate the two users' eigen-space with the EVD method. At this point, the EVD based blind suppression method cannot reduce the pilot contamination effectively.